def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(
rebalance,
TRADE_FREQ,
time_rules.market_open(minutes=1),
)
# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)
# Set up universe, alphas and ML pipline
context.universe = QTradableStocksUS()
ml_pipeline = make_ml_pipeline(
context.universe,
n_forward_days=PRED_N_FORWARD_DAYS,
window_length=ML_TRAINING_WINDOW,
)
# Create our dynamic stock selector.
attach_pipeline(ml_pipeline, 'alpha_model')
context.past_predictions = {}
context.hold_out_accuracy = 0
context.hold_out_log_loss = 0
context.hold_out_returns_spread_bps = 0
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_slippage(slippage.VolumeShareSlippage(volume_limit=0.025, price_impact=0.1)) # Default
set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.0)) # FSC for IB
schedule_function(
rebalance,
TRADE_FREQ,
time_rules.market_open(minutes=1),
)
# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)
# Set up universe, alphas and ML pipline
context.universe = Q1500US()
# if you are using IsAnnouncedAcqTarget, uncomment the next line
# context.universe &= IsAnnouncedAcqTarget()
ml_pipeline = make_ml_pipeline(
context.universe,
n_forward_days=PRED_N_FORWARD_DAYS,
window_length=ML_TRAINING_WINDOW,
)
# Create our dynamic stock selector.
attach_pipeline(ml_pipeline, 'alpha_model')
context.past_predictions = {}
context.hold_out_accuracy = 0
context.hold_out_log_loss = 0
context.hold_out_returns_spread_bps = 0
def initialize(context):
"""
Called once at the start of the algorithm.
"""
# Rebalance
algo.schedule_function(
rebalance,
algo.date_rules.month_end(), # SHOULD BE MONTH
algo.time_rules.market_close(minutes=30))
# Record tracking variables at the end of each day.
algo.schedule_function(record_vars, algo.date_rules.every_day(),
algo.time_rules.market_close(minutes=15))
# Create our dynamic stock selector.
algo.attach_pipeline(make_pipeline(), 'pipeline')
# Set commissions
algo.set_commission(commission.PerTrade(cost=19.0))
#algo.set_commission(commission.NoCommission())
#algo.set_commission(commission.PerShare(cost=.05))
context.use_weights = True # If weights should be used in optimization
context.filters = True # use filtering?
context.TF_lookback = 63 # How many days of SPY to look at
context.can_buy_stocks = False # Triggered by bullish market
context.can_buy = True
context.bonds = [symbol('IEF'), symbol('SHY'), symbol('TLT')]
context.num_stocks_to_trade = 20
context.roe_top_n = 50 # How many top roe companies to keep
context.momentum_days = 126 # How many days to check momentum
context.score_to_go = 30 # Minimum Momentum score to consider a share
context.days_to_skip = 10 # days before today to ignore in momentum